Method and apparatus for distributed control of thermostatic electric loads using high-granularity energy usage data

ABSTRACT

A system, apparatus, and method for distributed control of thermostatic electric loads (TELs) including receiving at an energy gateway, a demand response event signal and receiving real-time measurements of a temperature value and a power consumption value corresponding to a temperature setting of a plurality of TELs. The method retrieving historical data from pre-determined load profiles for the TELs and comparing load profiles and real-time measurements to determine a first consumption trajectory. Further, coordinating temperature settings of at least two TELs to generate a second consumption trajectory corresponding to the demand response event signal.

BACKGROUND OF THE INVENTION

1. Field

Embodiments of the present disclosure relate generally to control ofpower consumption, and, in particular, to distributed control ofthermostatic loads.

2. Description of the Related Art

Remotely controlling thermostatic electric loads (TELs) such as heating,ventilation, and air conditioning (HVAC) units in homes and businessesduring peak consumption hours has become a common practice of manyelectric power utilities. The control allows for issuing a demandresponse event signal that dynamically adjusts HVAC loads to conservepower and prevent overloading a power grid and ensure power distributionstability for the electric power utilities and consumers.

One method of direct TEL control has been to remotely adjust thetemperature set points of the loads to reduce energy consumption.Typically this method is implemented by installing an AM or FM receiverwith a relay on a heating unit or a cooling unit. A signal for a demandresponse event is then broadcast over the AM/FM network and induces thereceiver-relay to disconnect the load from the power grid. For example,the heating unit of a building during winter is controlled to allow ameasured temperature to drift lower a few degrees. Similarly, for acooling unit during summer, the temperature is allowed to drift upward afew degrees. The method may also rely on Internet-based communicationstandards instead of AM/FM broadcast infrastructure. However, TELcontrol based solely on temperature does not provide compensation forindividual loads, and merely lowers the amount of demanded power. Theresult is a lowered power demand, but the method is inefficient sincewithout specific temperature compensation correlated to power usagemonitoring, there are unbalanced loading cycles.

Therefore, there is a need in the art for a system, method, andapparatus that provides efficient control of thermostatic electric loadsbased on electric consumption for specific temperatures during demandresponse.

SUMMARY OF THE INVENTION

Embodiments of the present invention generally relate to a system,method, and apparatus for controlling thermostatic electric loads (TELs)using load profiles for load control during demand response as shown inand/or described in connection with at least one of the figures, as setforth more completely in the claims.

Various advantages, aspects and novel features of the presentdisclosure, as well as details of an illustrated embodiment thereof,will be more fully understood from the following description anddrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentinvention can be understood in detail, a more particular description ofthe invention, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIG. 1A is a diagram of an exemplary system for generating load profilesand demand response control in accordance with an embodiment of thepresent invention;

FIG. 1B is a block diagram of an exemplary demand response server of thesystem in FIG. 1A in accordance with an embodiment of the presentinvention;

FIG. 2 is block diagram of an exemplary controller in an energy gatewayoperating the load profile generation and demand response control systemdepicted in FIG. 1 in accordance with an embodiment of the presentinvention;

FIG. 3 is a flow diagram of an exemplary method for building loadprofiles in accordance with an embodiment of the present invention;

FIG. 4 is a flow diagram of an exemplary method for demand responseusing the load profiles in accordance with an embodiment of the presentinvention; and

FIGS. 5A and 5B are a comparative series of exemplary graphs ofdepicting load balancing in accordance with an embodiment of the presentinvention.

DETAILED DESCRIPTION

Metering infrastructure enables utilities to collect power and energymeasurements in repeated intervals (e.g., sub-minute intervals). Thislevel of resolution into a utility's customer energy usage patternsallows a new class of demand response products, services, and software.Monitoring using the repeated intervals allows for the generation ofload profiles based on historic data and preferences for buildings. Theload profiles correlate power consumption and desired thermostattemperature for each room, building, or groups of buildings, dependingon the desired granularity. The correlation is subsequently used toestablish a demand response with complimentary matching profiles so asto yield compensation that (from the perspective of the grid) has abalanced load trajectory during demand response events. With a balancedload trajectory, buildings may be operated over a wider range oftemperatures or be allowed to operate closer to a desired temperaturefor a longer duration while conserving energy as required by theutility.

FIG. 1A is a diagram of an exemplary system 100 for generating loadprofiles and demand response control in accordance with an embodiment ofthe present invention. The system 100 includes a communicationsinfrastructure in preparation for, and during a demand response (DR)event. The system 100 comprises multiple thermostatic electric loads(TELs) 101 _(N), energy gateways 103 _(N), an automated DR server 106,and a network 107 enabling the DR server 106 to communicate with otherservers 108 _(N). The network 107 may be wired, wireless, a local areanetwork (LAN), a wide area network (WAN), the Internet, or a combinationthereof.

TELs 101 _(N) include HVAC systems, heaters, air conditioners,refrigerators, chillers, and the like. TELs 101 _(N) can either be in anON state 104 or an OFF state 105. There may be several TELs 101 _(N) ona single premise (e.g., local area 115 ₁) or, alternatively, tied to asingle customer account. In the OFF state, a thermostatic load is notdrawing any power. The ON state 104 is composed of an initial transient“cold-load” pick-up, or surge in power consumption, followed by asteady-state power consumption level as the system (e.g., TEL 101 ₁)settles before TEL 101 ₁ is turned OFF 105 again.

Energy gateways 103 _(N) collect real-time sensor data from TELs 101_(N) (e.g., temperature, other weather, date, time, power consumed,consumption duration, and the like) and dispatch local control actionsfrom the DR server 106 on the TELs 101 _(N), such that each energygateway 103 _(N) corresponds to a local area 115 _(N). Each local area115 _(N), may correspond to a room, building, series of buildings, city,and the like for various load granularities. In other embodiments, theenergy gateways 103 _(N) can also communicate with non-thermostaticloads.

Communication signals between TELs 101 _(N) and DR server 106 with theenergy gateways 103 _(N) are passed over wireless protocols such as IEEE802.11 or 802.15 (ZIGBEE or SMART ENERGY PROFILE) or may be passed overother protocols such as ECHONET, BACNET, or MODBUS. In some embodiments,multiple energy gateways 103 _(N) are communicatively coupled as asingle resource under the management of the DR server 106 that maycommunicate over non-proprietary Internet-based protocols such as thoseoutlined under OPENADR. In some embodiments, the energy gateways 103_(N) are logical or virtual entities that operate as a software moduleeither on a virtual machine, base operating system, and existing energymanagement system, set-top box, or other hardware devices. An analyticsengine runs on the gateway for local-area control, or on the server forwide-area control. In other embodiments, energy gateways 103 _(N) mayinclude specifically designed software and ASICs.

Energy gateways 103 _(N) generate load profiles for each of the TELs 101_(N) as well as correlate the load profiles to a specific demandresponse received from the DR server 106. Load profiles are generatedusing historic data over a monitoring period (e.g., one month) thatdevelop a heuristic approach in profile generation. Historic monitoringassociates date, time, weather conditions, user preferences and the liketo develop load profiles that provide accurate correlations as to what aset TEL 101 _(N) temperature is required and how much power is consumedto maintain the temperature. In addition, load profiles may be operatedin the aggregate by the energy gateways 103 _(N) to yield a balancedload profile. The balanced load profile reduces strain on the grid, andmaximizes the power supplied during generation utilities.

DR server 106 securely interfaces with systems that define the loaddispatch, billing, aggregation parameters of each of the energy gatewaysand with supply-side resources for issuing control signals to improvethe reliability or economic efficiency of the grid. The DR server 106may interface with other servers 108 _(N) over the network 107. In someembodiments, other servers 108 _(N) include a price server and an energytrading platform for retail or wholesale electricity markets. In otherembodiments, the other servers 108 _(N) allow the DR server 106 tointerface with a load aggregation platform within or across serviceterritories (or load aggregation points in the case of deregulatedmarkets). Alternatively, the other servers 108 _(N) may also be billingand account servers of the electricity providers serving the customerswho own energy gateways that may interface with the DR server 106. Thus,the DR server 106 securely interfaces with systems that define the loaddispatch, billing, and aggregation parameters of each of the energygateways 103 _(N) and with supply-side resources for issuing controlsignals to TELs 101 _(N) to improve the reliability or economicefficiency of a power grid.

FIG. 1B is a block diagram of an exemplary demand response server 106 ofthe system in FIG. 1A in accordance with an embodiment of the presentinvention. The DR server 106 comprises a central processing unit (CPU)150, support circuits 154, and memory 156. The CPU 150 may be anycommercially available processor, microprocessor, microcontroller, andthe like. In other embodiments, the CPU 150 is a microcontroller such asa PIC. The support circuits 154 comprise well known circuits thatprovide functionality to the CPU 150 such as clock circuits,communications, cache, power supplies, I/O circuits, and the like.

The memory 156 may be any form of digital storage used for storing dataand executable software. Such memory includes, but is not limited to,random access memory, read only memory, disk storage, optical storage,and the like. The memory 156 stores computer readable instructionscorresponding to: demand response calculation module 162, and loadassignment module 164. Additional embodiments may include a telemetrymodule 160, an operating system 158 and one or more databases 166 storedin memory 156.

In some embodiments, the telemetry module 160 on the DR server 106receives sensor data for storage from energy gateways 103 _(N). As willbe further discussed below, alternative embodiments include generationof load profiles on the energy gateways 103 _(N). The telemetry module160 includes instructions to process data from TELs (e.g., TELs 101_(N)). Sensor data may include indoor and outdoor ambient temperaturesof a building and/or room, the thermostat temperature setting, and theamount of power consumed when a TEL 101 ₁ is in an ON state 104 for apre-determined period (e.g., less than a minute). The power consumptiondata is sampled at the steady-state power consumption level. Additionalembodiments may include sampling of the initial transient “cold-load”power surge when first turning on a TEL 101.

The telemetry module 160 may also aggregate global public backgroundinformation such as date, time, weather, and the like to correlate withthe power consumption level and thermostat temperature. Publicbackground information may be retrieved through the Internet. Otherembodiments include generating load profiles for monitoring andrecording energy consumption for operation between specific temperatureranges.

In some embodiments, the telemetry module 160 includes adjustingmeasurements with respect to specific user preferences. For example, auser that is a grocery store may specify, despite a demand responsecalling for all thermostats on a summer day to raise to 80 degreesFahrenheit the grocery store may have a fixed maximum of 75 degreesFahrenheit to impede mold growth. In another example, a gym sharing abuilding with an office may specify to over compensate to 83 degreesFahrenheit so as to allow the adjoining office to remain at a morecomfortable 77 degrees Fahrenheit. Generated load profiles based onthermostat temperature settings, actual measured temperatures, andaforementioned background data are stored in database 166.

In other embodiments, the telemetry module 160 receives actualtemperature sensor data directly from temperature sensors placed in thevicinity of a TEL 101 ₁ vent. In such an embodiment, the telemetrymodule 160 determines how effective cycling a thermostat between a giventemperature range is to reach a desired temperature. The telemetrymodule 160 also includes background data such as weather (e.g., coolerdays may only require fan operation) or day of the week (e.g., weekendsat stores may have greater foot traffic and constant air conditioning toa set temperature).

The demand response calculation module 162 includes instructions forprocessing a demand response event and calculating a correspondingresponse with load trajectory. The demand response calculation module162 is communicatively coupled to the telemetry module 160 and loadassignment module 164. The demand response calculation module 162retrieves load profiles stored in the database 166. In otherembodiments, the demand response calculation module 162 requests theload profile of a TEL 101 to be instantaneously read. Subsequently, thedemand response calculation module 162 determines the optimaltemperature setting for TELs 101 _(N) to achieve a target power demandas received from the DR server 106. The demand response calculationmodule 162 then instructs the energy gateways 103 _(N) to adjustspecific TELs 101 _(N) to a respective specific temperatures. Forexample, if a request is received to reduce loads to 1.00 kilowatt (kW)in a certain region or building, the demand response calculation module162 may control one building to cycle around 74 degrees Fahrenheit andanother building to 79 degrees Fahrenheit. The aggregate of the twospecifically controlled buildings results in an overall balanced loadreduction that would otherwise require other neighboring buildings alsoto raise temperatures to compensate for a demand response event.

In other embodiments, the demand response calculation module 162 mayinclude receiving real-time energy consumption data and indoortemperature data in addition to historical data. The real-time data isapplied to adjust in the system 100, specific TELs 101 _(N) to model aresponse to meet the demand requirements received from the other servers108 _(N) or utility provider.

The load assignment module 164 includes instructions for communicatingwith the energy gateways 103 _(N). Alternatively, the load assignmentmodule 164 includes instructions for communicating with the TELs 101_(N). The load assignment module 164 converts desired operatingtemperature signals from the demand response calculation module 162 intothe requisite communication signals necessary to control a specific TEL101 ₁. For example, the energy gateway 103 ₁ may be coupled to one TEL101 ₁ configured to receive commands wirelessly through IEEE 802.11(g)as well as another TEL 101 ₂ configured to receive commands through awired LAN connection or power line communication (PLC).

The load assignment module 164 also coordinates with the demand responsecalculation module 162 to determine which TELs 101 _(N) are to beadjusted to meet the calculated necessary load trajectory based onpre-determined profiles. For example, the load assignment module 164 maydetermine two buildings in one city are able to cycle at a much highertemperature because a load profile determined the TELs 101 _(N) of thetwo have more efficient chillers and fan capabilities than surroundingbuildings. As a result, the two buildings can cycle near a highertemperature to reduce overall grid power demand such that multiplesurrounding buildings may operate closer to a desired lower temperature.

FIG. 2 is block diagram of an exemplary controller 200 in an energygateway 103 _(N) operating the load profile generation and demandresponse control system depicted above in FIG. 1 in accordance with anembodiment of the present invention.

The controller 200 comprises a central processing unit (CPU) 202,support circuits 204, and memory 208. The CPU 202 may be anycommercially available processor, microprocessor, microcontroller, andthe like. In other embodiments, the CPU 202 is a microcontroller such asa PIC. The support circuits 204 comprise well known circuits thatprovide functionality to the CPU 202 such as clock circuits,communications, cache, power supplies, I/O circuits, and the like.

The memory 206 may be any form of digital storage used for storing dataand executable software. Such memory includes, but is not limited to,random access memory, read only memory, disk storage, optical storage,and the like. The memory 206 stores computer readable instructionscorresponding to: a load profile generation module 210, demandcommunication module 212, and load control module 214. Additionalembodiments may include an operating system 208 and one or moredatabases 216 stored in memory 206.

The load profile generation module 210 includes instructions to processdata from TELs (e.g., TELs 101 _(N)). Sensor data may include indoor andoutdoor ambient temperatures of a building and/or room, the thermostattemperature setting, and the amount of power consumed when a TEL 101 ₁is in an ON state 104 for a pre-determined period (e.g., less than aminute). The power consumption data is sampled at the stead-state powerconsumption level. Additional embodiments may include sampling of theinitial transient “cold-load” power surge when first turning on a TEL101. The load profile generation module 210 may also aggregate globalpublic background information such as date, time, weather, and the liketo correlate with the power consumption level and thermostattemperature. Other embodiments include generating load profiles formonitoring and recording energy consumption for operation betweenspecific temperature ranges.

In some embodiments, the load profile generation module 210 includesspecific user preferences. For example, a user that is a grocery storemay specify, despite a demand response calling for all thermostats on asummer day to raise to 80 degrees Fahrenheit the grocery store may havea fixed maximum of 75 degrees Fahrenheit to impede mold growth. Inanother example, a gym sharing a building with an office may specify toover compensate to 83 degrees Fahrenheit so as to allow the adjoiningoffice to remain at a more comfortable 77 degrees Fahrenheit. Generatedload profiles based on thermostat temperature setting, actual measuredtemperature, and aforementioned background data are stored in database216.

In other embodiments, energy gateways 103 _(N) may upload load profilesand system measurements to the DR server 106 to conserve memoryresources. Alternatively, the load profiles and system measurements maybe uploaded to a telemetry module on the DR server 106. In such anembodiment, the telemetry module 160, organizes the measurements forcoordination of a load trajectory communicated to the communicationmodule 212.

In other embodiments, the load profile generation module 210 receivesactual temperature sensor data from temperature sensors placed in thevicinity of a TEL 101 ₁ vent. In such an embodiment, the load profilegeneration module 210 determines how effective cycling a thermostatbetween a given temperature range is to reach a desired temperature. Theload profile generation module 210 also includes background data such asweather (e.g., cooler days may only require fan operation) or day of theweek (e.g., weekends at stores may have greater foot traffic andconstant air conditioning to a set temperature).

The demand communication module 212 processes communication exchangeswith the DR server 106. The demand communication module 212 sendsmeasurement data to the DR server 106 and processes commands for a loadprofile to respective TELs 101 _(N). The demand communication module isconfigured to receive communications through wireless, cellular, wiredLAN network connections or power line communication (PLC) from the DRserver 106. In some embodiments, the communications with the DR server106 are done through secure communication protocols or may requireauthentication into the DR server 106.

In other embodiments, the demand communication module 212 may includereceiving real-time energy consumption data and indoor temperature datain addition to historical data. The real-time data is applied to adjustin the system 100, specific TELs 101 _(N) to model a response to meetthe demand requirements received from the DR server 106.

The load control module 214 includes instructions for communicating withthe TELs 101 _(N). The load control module 214 converts desiredoperating temperature signals from the DR server 106 for a calculatedload trajectory into the requisite communication signal necessary tocontrol a specific TEL 101 ₁. For example, the energy gateway 103 ₁ maybe coupled to one TEL 101 ₁ configured to receive commands wirelesslythrough IEEE 802.11(g) as well as another TEL 101 ₂ configured toreceive commands through a wired LAN connection or power linecommunication (PLC).

FIG. 3 is a flow diagram of an exemplary method 300 for building loadprofiles in accordance with an embodiment of the present invention. Themethod 400 is implemented by the DR server 106 and energy gateways 103_(N) and system 100 described above. Load profiles are initially builtduring an observational period spanning months prior to deployment in ademand response event. In addition, established load profiles may becontinually updated over time.

The method 300 begins at step 305 and continues to step 310. At step310, temperature and power consumption data from sensors is received.Temperature data sampled includes the thermostat setting, indoor ambienttemperature, and outdoor temperature. Power consumption data includeskW, kilowatt hour (kWh), instantaneous current, instantaneous voltage,and the like. The sampling rate of sensor data has a higher frequencythan the duty-cycle of an exemplary TEL 101 _(N). For example, a rooftopAC unit cycles on/off every 15 minutes to maintain a constant indoortemperature. In such an example, to properly measure power andtemperature data, sampling must be at a rate higher than once per 15minutes such as once every 2, 4, 30 seconds or 5 minutes and the like.

Next, at step 315, background data is received. Background data includespublic weather data, address, TEL unit information, time, date,geographic location, elevation and the like.

Next, at step 320 power consumption data is associated with thetemperature data and other received data from step 315. For example, a1200 watt TEL 101 ₁ operating in a single-family unit during the heat ofsummer when the outdoor ambient temperature is 101 degrees may require 4kWh to maintain a temperature at 68 degrees but 2 kWh to maintain atemperature of 70 degrees for a day. The same TEL 101 ₁ may require 1kWh to maintain a temperature at 70 degrees when the outdoor ambienttemperature is 80 degrees for a day.

Then at step 325, the method 300 receives user preferences. Userpreferences may include specific temperature ranges that must bemaintained throughout the day or for a time of day.

Next, at step 330, all data is aggregated into a load profile based onpower consumption over an observation period. By aggregating data overtime, the load profile includes load trajectories for specific TELs 101_(N) to maintain a specific temperature during the operating environmentas determined from the background data. Similarly, certain data may beflagged in a load profile for anomalous events rare events such asnatural disasters and given less importance in a profile.

Optionally, at step 335, load profiles may be correlated to an operatingregion. Load profiles for TELs 101 _(N) may be correlated and grouped bylocation to allow faster allocation of resources or adjustments of loadswithin the grid. For example, user accounts or TELs 101 _(N) with thesame zip code may be correlated together for fine control during ademand response within a county.

At step 340, the load profiles are stored in memory as historical datafor assigned TELs 101 _(N). The method 300 proceeds to step 345 todetermine whether to continue building and/or updating load profiles. Ifa determination is made to continue, the method 300 reverts to step 310.If however, a determination is made not to continue, the method 300 endsat step 350.

FIG. 4 is a flow diagram of an exemplary method 400 for demand responseusing the load profiles in accordance with an embodiment of the presentinvention. The method 400 is implemented by system 100, energy gateways103 _(N) and controllers described above.

The method 400 begins at step 405 and continues to step 410. At step410, a demand response event is received from a utility or DR server106. In some embodiments, a load trajectory is calculated to meet therequirements of the demand response event. Next, at step 415, a requestfor real-time power consumption and temperature data is made to the TELs101 _(N).

Next, at step 420, select load profiles with historical data isretrieved from the database 216 for respective TELs 101 _(N). Theselected load profiles are those corresponding to TELs 101 _(N) of aregion that is receiving the demand response event signal.

Then at step 425, the method 400 calculates the trajectory of thecurrent power consumption by the TELs 101 _(N). Calculations includecomparing historical data in the load profiles to that of therequirements from the desired demand event. For example, historical dataassociates the amount of power consumed to operate in a specifictemperature range. Thus, the amount of power drawn by a specific TEL 101₁ may be predicted if operated at a specific temperature. The predictionis further defined based on background data in the load profilediscussed above. In addition, as will be discussed in FIG. 5 below,calculations also include summing multiple load profile waveformscorresponding to power usage.

In some embodiments, parameters for determining load trajectory arecalculated based on thermal capacitance and resistance of specific TELs101 _(N). Thermal characteristics of each TEL 101 _(N) may be determinedby Equation 1:

a= ^(e−h/(CR))  (1)

In the above Equation 1, parameter “a” represents the thermalcharacteristic of a TEL 101. Parameters “C” and “R” are respectively thethermal capacitance and resistance of the TEL 101 _(N) and “h” is a timestep.

The transition or evolution of the indoor temperature in the next timestep is a function of current indoor temperature, ambient outdoortemperature, and temperature gain provided in Equations 2 and 3:

T _(indoor,t+1) =aT _(indoor,t)+(1−a)(T _(outdoor) −uT _(gain))+ε  (2)

T _(gain) =RP _(rate)  (3)

In Equations 2 and 3, T_(gain) is always a non-negative number, and E israndom temperature noise. The parameter “u” is either 0 or 1 that isrepresentative of either an OFF state or ON state of the TEL 101 _(N).If T_(gain) is positive then the TEL 101 _(N) is operating as a coolingunit and therefore driving the indoor temperature down when it is in theON state (i.e. u=1). Similarly, T_(gain) is negative when the TEL isoperating as a heating unit.

Since the system 100 does not know C, R, and ε a priori, these valuesmust be “learned” over time (i.e., stored and calculated measurementsaccumulated over an observational time period). By collecting historictemperature and power data and performing semi-parametric regression onT_(indoor), T_(outdoor), and P_(rate), the value of C, R, and ε may beestimated. Once sufficient data for a specified observational timeperiod (e.g., days, weeks, months, seasons, years, and the like) hasbeen collected and analyzed, a model for resolving a predictive controlproblem may be established for determining load trajectories and loadprofiles. In some embodiments, the values of the parameters may beadjusted as the values are subject to the uncertainty tolerance of thegrid operator. The model for the predictive control is represented byx_(t) in Equation 4:

x _(t+1) =Cx _(t) +Du _(t)  (4)

In the aforementioned Equation 4, the value of parameter x_(t)represents a vector temperature, and power states for all TELs 101 _(N).A parameter u_(t) is a vector value of control states composed on 0's(OFF state) and 1's (ON state). For example, x=[28 29 24 27] representsin Celsius, four TELs with the individual temperature states of 28° C.,29° C., 24° C., and 27° C. The estimated power states are a function ofthe of the “u” vector, (e.g., if u=[0 0 1 0] then all but one of fourTELs is turned OFF).

The parameter “C” is a matrix derived from the temperature dynamicsdescribed in the above Equations 1-4. The parameter “B” is a matrix ofrepresenting the influence of the respective TEL control states in thesystem 100 (e.g., all TELs 101 _(N) coupled to the DR server 106). Theparameter x_(t+1) represents the predicted states of each of the TELs101 _(N). In general, u_(t) is aleatoric and substantially determined bythe individual preferences of the TEL users (e.g., home owners, buildingtenants, and the like). However, when a DR event signal is dispatchedfrom the DR server 106 to the energy gateways 103 _(N) and TELs 101_(N), the values of “u_(t)” are selected as to control the sum of allvalues of P_(rate) in Equation 3 for all TELs 101 _(N) within the system100. The selections of the “u” values are based on a desired aggregatepower consumption level of the grid operator communicated to the DRserver 106. The load trajectory is thus determined so as operative toestablish the desired aggregate power consumption level provided by thegrid operator or utility provider (e.g., other servers 108 _(N)).

At step 430, load profiles are selected and aggregated to be coordinatedfor new temperatures and power consumption that conform a new loadtrajectory that corresponds to the utility demand event received fromthe DR server 106. By adjusting thermostat temperature, and schedulingthe timing of cycling between ON states 104 and OFF states 102, a newload trajectory is generated for TELs 101 _(N). The cumulative profileresults in a trajectory is a balanced load correlating to the desireddemand event.

At step 435, the method sends the corresponding temperature adjustmentsto the TELs 101 _(N) that are correlated to previous historical dataenergy consumption loads. For example, a previous load profile for a TEL101 ₁ may show a steady-state operation of 0.8 kW for a temperature of78 degrees. Continuing the example, a previous load profile for a TEL101 ₂ may show a steady-state operation of 0.2 kW for a temperature of75 degrees. The net operation of the TELs 101 ₁ and 101 ₂ would meet anew trajectory requirement of 1 kW.

Next at step 440, the method requests real-time power consumption andtemperature data. This second sampling of data is used to determine theeffectiveness of the newly implemented trajectory in step 445.

At step 445, the method 400 determines whether the temperatureadjustments to the TELs 101 _(N) was effective in meeting the demandresponse event requirement. In some embodiments, meeting the requirementmay have a pre-determined acceptable error tolerance (e.g., +/−2%). Ifit is determined the adjustment is insufficient, the method 400 revertsback to step 425. If however, the adjustment is sufficient, the method400 continues to step 450.

At step 450, the method 400 determines whether the demand response eventis still active. If determined to be still active, the method 400reverts back to step 425. In most instances, the events are temporarymeasurements taken by power utilities to prevent blackouts. Once anevent is signaled as over or the event signal is no longer received fromthe DR server 106, the method 400 determines the event is not active andthe method 400 ends at step 455.

FIGS. 5A and 5B is a comparative series of exemplary graphs of depictingload balancing in accordance with an embodiment of the presentinvention. FIGS. 5A and 5B includes plots of measured temperature indegrees Fahrenheit, power in kW, and energy as kWh (depicted as the areaunder the power curve). FIG. 5A includes graphs 510, 515, 520, 525representing TELs 101 _(N) prior to temperature adjustments when thereis no demand response event corresponding to exemplary load profiles.Graphs 510, 515, and 520 are plots of power 502 and temperature 504 overtime 505. Graph 525 is a plot of power 522 over time 526.

Graphs 510, 515, and 520 correspond to exemplary TELs 101 _(N). Graph510 plots historical data for TEL 101 ₁ with an indoor temperaturewaveform 512 separately measured from a temperature sensor, acorresponding recorded consumed power curve 514 is shown for operatingan associated HVAC between 71 and 72 degrees. The temperature sensor isin addition to any standard thermostat sensors in the TELs 101 _(N).Graph 515 plots historical data for TEL 101 ₂ with an indoor temperaturewaveform 517 and corresponding recorded consumed power curve 519 foroperating an associated HVAC between 71 and 73 degrees. Graph 520 plotshistorical data for TEL 101 _(N) with a temperature waveform 518 andcorresponding recorded consumed power curve 516 for operating anassociated HVAC between 70 and 71 degrees. From the graph 520, TEL 101_(N) consumes much more power to maintain a temperature point range andis either a much larger or inefficient HVAC system than that of TEL 101₁ and TEL 101 ₂. Graph 525 is an aggregate of graphs 510, 515, and 520that indicate a net consumption waveform 524 that has a generallyhaphazard inefficient trajectory.

FIG. 5B includes graphs 540, 545, 550, 555 representing TELs 101 _(N)controlled during a demand response event. Graphs 540, 545, and 550 areplots of power 532 and temperature 532 over time 535. Graph 555 is aplot of power 558 over time 536.

Graph 540 plots real-time data for TEL 101 ₁ with an indoor temperature542 and corresponding measured consumed power curve 544. With the DRevent active, a comparison of graph 540 to 510 indicates operating in atemperature range between 72 and 73 degrees consumes less energy (e.g.,width of consumed power curve 544 from t0 to t1). In addition, thecycling of the associated HVAC system with TEL 101 ₁ shifts the energyconsumption as shown by a comparison of peaks of the consumed powercurves 514 and 544.

Graph 545 plots real-time data for TEL 101 ₂ with an indoor temperaturewaveform 546 and corresponding measured consumed power curve 548.Similarly to TEL 101 ₁, during a DR event, the TEL 101 ₂ is controlledto operate between 72 and 73 degrees. A comparison of graphs 545 and 515show decreased energy consumption between curves 519 and 548.

Graph 550 comprises real-time data for TEL 101 _(N) with a temperaturewaveform 551 and corresponding measured consumed power curve 549. Fromthe graph 520, TEL 101 _(N) consumes less power as compared to graph520. Although TEL 101 _(N) is controlled to operate across a higherdegree range (e.g., 70.5 degrees to 72 degrees), the ratio totemperature and energy consumed is smaller and thus TEL 101 _(N) isoperated more efficiently.

Graph 555 is an aggregate of graphs 540, 545, and 550 that indicate anet consumed power curve 557 that is a stable load. The shifting ofconsumption peaks in curves 544, 548, and 549 as resultant from theactive DR event compensation described above yields the consumed powercurve 557 to be of a more stabilized load trajectory as compared tocurve 524.

The foregoing description of embodiments of the invention comprises anumber of elements, devices, circuits and/or assemblies that performvarious functions as described. These elements, devices, circuits,and/or assemblies are exemplary implementations of means for performingtheir respectively described functions.

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof is definedby the claims that follow.

1. A method for distributed control of thermostatic electric loads(TELs) using load profiles comprising: receiving at an energy gateway, ademand response event signal; receiving real-time measurements of atemperature value and a power consumption value corresponding to atemperature setting of a plurality of TELs; retrieving historical datafrom pre-determined load profiles for the TELs; comparing load profilesand real-time measurements to determine a first consumption trajectory;and coordinating temperature settings of at least two TELs to generate asecond consumption trajectory corresponding to the demand response eventsignal.
 2. The method of claim 1, wherein historical data includesbackground data for the TELs.
 3. The method of claim 2, whereinhistorical data includes at least one of weather, time, date, orgeographic location.
 4. The method of claim 1, wherein coordinatingtemperature settings further comprises adjusting temperature settingsfor TELs based on historic energy consumption.
 5. The method of claim 4,further comprising scheduling when each TEL cycles to an ON state and aduration of a steady-state operation.
 6. The method of claim 1, whereinload profiles are generated using received temperature sensor data overan observation period and aggregated according to a power consumptionlevel.
 7. The method of claim 6, wherein load profiles record backgrounddata and associate the background data to the temperature sensor data.8. The method of claim 1, further comprising wherein the demand responseevent signal is from a demand response server.
 9. An energy gatewayapparatus for distributed control of thermostatic electric loadscomprising: a) at least one processor; b) at least one input devicecoupled to at least one network; and c) at least one storage devicestoring processor executable instructions comprising: i. a load profilegeneration module operative to generate load profiles; ii. a demandresponse calculation module operative to request real-time measurementsof a temperature value and a power consumption value corresponding to atemperature setting of a plurality of TELs upon receiving a demandresponse event signal, retrieving historical data from load profiles forthe TELs, comparing load profiles and real-time measurements todetermine a first consumption trajectory, and coordinating temperaturesettings of at least two TELs to generate a second consumptiontrajectory corresponding to the demand response event signal; and iii. aload control module for selectively controlling the plurality of TELsbased on the second consumption trajectory.
 10. The apparatus of claim9, further comprising a database module storing historical data thatincludes background data for the TELs.
 11. The apparatus of claim 10,wherein historical data includes at least one of weather, time, date, orgeographic location.
 12. The apparatus of claim 9, wherein the demandresponse calculation module further adjusts temperature settings forTELs based on historic energy consumption.
 13. The apparatus of claim12, further comprising storing processor executable commands to schedulewhen each TEL cycles to an ON state and a duration of a steady-stateoperation.
 14. The apparatus of claim 9, wherein stored load profilesfurther record background data and associate the background data totemperature sensor data.
 15. The apparatus of claim 9, furthercomprising wherein the demand response event signal is from a demandresponse server.
 16. A system for distributed control of thermostaticelectric loads (TELs) comprising: a demand response server; a pluralityof TELs; a plurality of energy gateways, each coupled to the at leastone TEL, such that each energy gateway comprises a controller with atleast one processor and at least one storage device storing processorexecutable instructions which, when executed by the at least oneprocessor, performs a method including: receiving a demand responseevent signal from the demand response server; receiving real-timemeasurements of a temperature value and a power consumption valuecorresponding to a temperature setting of a plurality of TELs;retrieving historical data from pre-determined load profiles for theTELs; comparing load profiles and real-time measurements to determine afirst consumption trajectory; and coordinating temperature settings ofat least two TELs to generate a second consumption trajectorycorresponding to the demand response event signal.
 17. The system ofclaim 16, wherein the TELs comprise at least one of HVAC systems,heaters, air conditioners, refrigerators, and commercial chillers. 18.The system of claim 16, wherein coordinating temperature settingsfurther comprises adjusting temperature settings for TELs based onhistoric energy consumption and scheduling when each TEL cycles to an ONstate and a duration of a steady-state operation.
 19. The system ofclaim 16, wherein load profiles are generated using received temperaturesensor data over an observation period and aggregated according to apower consumption level.
 20. The system of claim 19, wherein loadprofiles record background data and associate the background data to thetemperature sensor data.